Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/adriannoes/awesome-agentic-ai/remembergit clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/adriannoes/awesome-agentic-ai/remember)<a href="https://agentmods.dev/commands/adriannoes/awesome-agentic-ai/remember"><img src="https://agentmods.dev/badge/commands/adriannoes/awesome-agentic-ai/remember.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00024 | $0.00436 |
| Opus 5 | $0.00012 | $0.00218 |
| Sonnet 5 | $0.00005 | $0.00087 |
| Haiku 4.5 | $0.00002 | $0.00044 |
Grade A, and why
remember scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to remember — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
/remember
Save a finding or successful pattern to persistent hunt memory.
What This Does
- Auto-populates fields from session context (target, endpoint, vuln_class, technique)
- If
/validatewas run in this session, pre-fills from validation output - Prompts you to confirm or edit before saving
- Writes to
journal.jsonl(always) +patterns.jsonl(if confirmed + payout > 0) - Updates the target profile's
tested_endpointsandfindings
Usage
/remember # after finding something
/remember --from-validate # explicitly pull from last /validate
Interactive Flow
REMEMBER — Log finding to hunt memory
Target: target.com (auto-detected)
Endpoint: /api/v2/users/{id}/orders (from session)
Vuln Class: idor (from session)
Technique: numeric_id_swap_with_put_method
Result: [confirmed / rejected / partial / informational]?
Severity: [critical / high / medium / low]?
Payout: $___?
Notes: ___?
Tags: [comma-separated]?
Save to hunt memory? [y/n]
Minimum Required Fields
- target
- vuln_class
- endpoint
- result
What Gets Written
| Field | journal.jsonl | patterns.jsonl | target profile |
|---|---|---|---|
| Finding details | Always | If confirmed + payout > 0 | findings[] updated |
| Tested endpoint | — | — | tested_endpoints[] updated |
| Tech stack | — | From target profile | — |
Why This Matters
- Next time you hunt a target with similar tech stack, your successful patterns are suggested first
/pickup target.comshows which endpoints you've tested and which remain- Cross-target learning: patterns from target A inform hunting on target B
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 63 lines · 0 tokens per session scan A c7de20c0cd8a
remember is a command published in the GitHub repository adriannoes/awesome-agentic-ai (55 stars, last pushed 8d ago), licensed MIT. It adds 24 tokens to every session and 436 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to remember, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
context-stats
Display context window usage and token statistics.
handoff
Create a handoff document for seamless session continuity.
reconcile
Reconcile learnings into .claude/rules/ proposals — on-demand version of session-end Phase 3.6.8.
evolve
Extract session patterns into reusable learnings.
lessons
Lists the feedback memories captured for the current project (and globally) — the "lessons" learned from user corrections.
generate-project-context
Create project-context.md with AI rules. Use when the user says ""generate project context"" or ""create project context"".